def to_keras(self):
"""Convert a Onnx model to KerasNet model.
"""
# parse network inputs, aka parameters
for init_tensor in self.graph.initializer:
if not init_tensor.name.strip():
raise ValueError("Tensor's name is required.")
self.initializer[init_tensor.name] = OnnxInput(
name=init_tensor.name, zvalue=OnnxHelper.to_numpy(init_tensor))
# converting GraphProto message
# i: ValueInfoProto
for i in self.graph.input:
if i.name in self.initializer:
# we should have added that via graph._initializer
self._all_tensors[i.name] = self.initializer[i.name]
else:
self._inputs[i.name] = OnnxInput(
name=i.name, zvalue=OnnxHelper.get_shape_from_node(i))
self._all_tensors[i.name] = self._inputs[i.name]
# constructing nodes, nodes are stored as directed acyclic graph
# converting NodeProto message
for node in self.graph.node:
inputs = []
for i in node.input:
if i == "":
continue
if i not in self._all_tensors:
raise Exception("Cannot find {}".format(i))
inputs.append(self._all_tensors[i])
mapper = OperatorMapper.of(node, self.initializer, inputs)
# update inputs and all_tensors
for input in mapper.model_inputs:
# Only update the original inputs here.
if input.name in self._inputs:
self._inputs[input.name] = input.zvalue
self._all_tensors[input.name] = input.zvalue
tensor = mapper.to_tensor()
output_ids = list(node.output)
assert len(output_ids) == 1 or node.op_type == "Dropout",\
"Only support single output for now"
self._all_tensors[output_ids[0]] = OnnxInput(name=tensor.name,
zvalue=tensor)
output_tensors = []
for i in self.graph.output:
if i.name not in self._all_tensors:
raise Exception("The output haven't been calculate")
output_tensors.append(self._all_tensors[i.name].zvalue)
model = zmodels.Model(input=list(self._inputs.values()),
output=output_tensors)
return model
def _extract_model_inputs(self):
"""
:return: list of OnnxInput
"""
input = OnnxInput(name=self.op_name, zvalue=OnnxHelper.to_numpy(self.onnx_attr['value']))
return [self._to_zoo_input(input, is_constant=True)]